AI-Powered Solutions for Multi-Player Sports Tracking in Real-Time

The project goal was to replace the human operator and automatically find the most interesting area on a hockey, football, or basketball pitch. The It-Jim team has built a large-scale system for real-time sports analytics. 

Input: Video stream

Output: Player’s positions, ROI

Technologies & Skills:

• Computer Vision
• Machine Learning
• Real-time Analytics
• ROI Control Algorithm
• Programming Language – C/C++

Project Challenges & Functionality

We used a region-of-interest (ROI) control algorithm to ensure accurate tracking of players in real time.

The system has several key features:

• Pitch mask segmentation prevents false detections of the player blobs.
• Real-time panorama stitching combines images from multiple cameras.
• Adaptive filtering and thresholding help find player candidates.
• A custom tracker accurately follows player movements.
• A unique clustering method helps with smooth and logical ROI selection and control.

Solution & Results

We have designed, developed, and deployed a large-scale system for real-time sports analytics. As a result, it was possible to replace the human operator and automatically keep track of the sports game.

The main advantage of this multi-sports tracker lies in its **high performance**. It runs at 120 FPS (frames per second) and can work in many cloud instances. This makes the solution cost-effective, flexible, and capable of real-time ROI tracking and streaming for clients worldwide.

Highlights:

• Multiple objects are tracked in real-time.
• The tracking system combines results from multiple cameras.
• The AI-based solution provides data for sports analytics.
• Automatic extraction of the region of interest for live streaming.